This notebook contains a set of analyses for analyzing fionajackilarious’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
fionajackilarious | training | published before 2020 | 154 | 154 |
fionajackilarious | validation | published 2020 | 18 | 19 |
fionajackilarious | test | published after 2020 | 8 | 12 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
fionajackilarious | Unknown | 9.1% | 0.8% | 11.02 |
fionajackilarious | Asmodee | 16.9% | 2.5% | 6.66 |
fionajackilarious | Combinatorial | 10.4% | 1.6% | 6.56 |
fionajackilarious | Pegasus Spiele | 13.6% | 2.1% | 6.36 |
fionajackilarious | Milton Bradley | 10.4% | 1.9% | 5.36 |
fionajackilarious | Parker Brothers | 12.3% | 2.4% | 5.18 |
fionajackilarious | Hasbro | 14.3% | 2.9% | 4.96 |
fionajackilarious | Kosmos | 8.4% | 1.9% | 4.34 |
fionajackilarious | Word Game | 9.1% | 2.2% | 4.11 |
fionajackilarious | Network And Route Building | 9.1% | 2.5% | 3.66 |
fionajackilarious | Medieval | 11.7% | 4.6% | 2.54 |
fionajackilarious | Print Play | 6.5% | 2.8% | 2.31 |
fionajackilarious | Card Game | 45.5% | 29.4% | 1.55 |
fionajackilarious | Two Player Only Games | 24.7% | 17.4% | 1.42 |
fionajackilarious | Memory Game | 0.0% | 2.6% | 0.00 |
fionajackilarious | Simulation | 0.0% | 10.2% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 400 | 2136 | Pachisi | 0.757 | no |
2 | 1893 | 2386 | Chinese Checkers | 0.756 | no |
3 | 2011 | 70919 | Takenoko | 0.731 | yes |
4 | 1959 | 7688 | Memory | 0.689 | no |
5 | 1861 | 11670 | The Game of Authors | 0.683 | no |
6 | 2016 | 205398 | Citadels | 0.666 | no |
7 | 2015 | 175878 | 504 | 0.654 | no |
8 | 2016 | 169786 | Scythe | 0.646 | no |
9 | 2019 | 270971 | Era: Medieval Age | 0.630 | no |
10 | 1150 | 2083 | Checkers | 0.628 | yes |
11 | 2019 | 286096 | Tapestry | 0.588 | no |
12 | 1998 | 503 | Through the Desert | 0.562 | no |
13 | 1968 | 7262 | Top Trumps | 0.555 | no |
14 | 2015 | 178900 | Codenames | 0.554 | no |
15 | 1987 | 526 | Abalone | 0.539 | no |
16 | 1948 | 320 | Scrabble | 0.526 | yes |
17 | 1931 | 2425 | Battleship | 0.526 | no |
18 | 2000 | 823 | The Lord of the Rings | 0.522 | no |
19 | 2003 | 6068 | Queen's Necklace | 0.503 | no |
20 | 2013 | 146818 | Cappuccino | 0.491 | no |
21 | 1800 | 45 | Perudo | 0.471 | no |
22 | 2014 | 148228 | Splendor | 0.468 | yes |
23 | 1887 | 15878 | Rummy | 0.464 | no |
24 | 2018 | 199792 | Everdell | 0.453 | no |
25 | 1967 | 3656 | Score Four | 0.453 | no |
26 | 2014 | 157354 | Five Tribes | 0.442 | no |
27 | 1942 | 4112 | Hex | 0.442 | no |
28 | 1977 | 811 | Rummikub | 0.441 | no |
29 | 2008 | 33107 | Senji | 0.435 | no |
30 | 1475 | 171 | Chess | 0.432 | yes |
31 | 2005 | 14996 | Ticket to Ride: Europe | 0.430 | no |
32 | 2010 | 65200 | Asteroyds | 0.426 | no |
33 | 1995 | 13 | Catan | 0.404 | yes |
34 | 2019 | 285984 | Last Bastion | 0.382 | no |
35 | 1999 | 204 | Stephenson's Rocket | 0.376 | no |
36 | 2016 | 198487 | Smash Up: Cease and Desist | 0.376 | no |
37 | 1946 | 1917 | Stratego | 0.355 | no |
38 | 2012 | 125311 | Okiya | 0.329 | no |
39 | 2013 | 134453 | The Little Prince: Make Me a Planet | 0.320 | no |
40 | 2004 | 9209 | Ticket to Ride | 0.306 | yes |
41 | 1990 | 2379 | Guesstures | 0.306 | yes |
42 | 2005 | 15062 | Shadows over Camelot | 0.305 | no |
43 | 2017 | 220775 | Codenames: Disney – Family Edition | 0.305 | no |
44 | 2014 | 163412 | Patchwork | 0.303 | yes |
45 | 2010 | 62219 | Dominant Species | 0.301 | no |
46 | 1883 | 2389 | Othello | 0.301 | no |
47 | 2018 | 245638 | Coimbra | 0.294 | no |
48 | 2014 | 132531 | Roll for the Galaxy | 0.294 | no |
49 | 2005 | 16395 | Blokus Duo | 0.282 | no |
50 | 2012 | 118418 | Divinare | 0.275 | no |
51 | 2017 | 197376 | Charterstone | 0.274 | no |
52 | 1977 | 1295 | Pente | 0.273 | no |
53 | 1989 | 1111 | Taboo | 0.271 | yes |
54 | 2009 | 56885 | The Werewolves of Miller's Hollow: The Village | 0.266 | no |
55 | 2012 | 120677 | Terra Mystica | 0.266 | yes |
56 | 1994 | 18 | RoboRally | 0.265 | no |
57 | 1998 | 116 | Guillotine | 0.260 | no |
58 | 2008 | 38159 | Ultimate Werewolf: Ultimate Edition | 0.258 | no |
59 | 1910 | 3359 | Anagrams | 0.249 | no |
60 | 2017 | 221805 | Breaking Bad: The Board Game | 0.249 | no |
61 | 1999 | 111 | Rheinländer | 0.244 | no |
62 | 1997 | 42 | Tigris & Euphrates | 0.238 | no |
63 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.232 | no |
64 | 2009 | 39683 | At the Gates of Loyang | 0.226 | no |
65 | 1992 | 118 | Modern Art | 0.221 | no |
66 | 2004 | 9446 | Blue Moon | 0.215 | no |
67 | 2011 | 79828 | A Few Acres of Snow | 0.212 | no |
68 | 1977 | 2593 | Pass the Pigs | 0.212 | no |
69 | 2016 | 192312 | Mr. Cabbagehead's Garden | 0.212 | no |
70 | 2014 | 160477 | Onitama | 0.211 | no |
71 | 1996 | 278 | Catan Card Game | 0.210 | no |
72 | -1300 | 11901 | Tic-Tac-Toe | 0.209 | no |
73 | 1995 | 46 | Medici | 0.209 | no |
74 | 1995 | 929 | The Great Dalmuti | 0.209 | no |
75 | 2005 | 17405 | 1846: The Race for the Midwest | 0.208 | no |
76 | 2005 | 17119 | Head-to-Head Poker | 0.208 | no |
77 | 2017 | 162886 | Spirit Island | 0.204 | no |
78 | 2005 | 21022 | Was'n das? | 0.201 | no |
79 | 2011 | 84876 | The Castles of Burgundy | 0.200 | no |
80 | 2000 | 2655 | Hive | 0.194 | no |
81 | 2006 | 22245 | Royal Visit | 0.194 | yes |
82 | 2012 | 127997 | Qin | 0.191 | no |
83 | 1979 | 4143 | Guess Who? | 0.188 | no |
84 | 2010 | 154597 | Hive Pocket | 0.188 | yes |
85 | 2000 | 2453 | Blokus | 0.184 | no |
86 | 2012 | 118048 | Targi | 0.183 | yes |
87 | 2017 | 216658 | Smash Up: What Were We Thinking? | 0.182 | no |
88 | 2017 | 220308 | Gaia Project | 0.180 | no |
89 | 2010 | 73439 | Troyes | 0.178 | no |
90 | 2011 | 109786 | Serica: Plains of Dust | 0.178 | no |
91 | 2016 | 198454 | When I Dream | 0.175 | no |
92 | 2015 | 177639 | Raptor | 0.173 | no |
93 | 2018 | 223514 | Coin & Crown | 0.171 | no |
94 | 2008 | 40508 | Scrabble Slam! | 0.171 | no |
95 | 2014 | 152241 | Ultimate Werewolf | 0.170 | no |
96 | 2000 | 491 | Web of Power | 0.169 | no |
97 | 2017 | 233078 | Twilight Imperium: Fourth Edition | 0.168 | no |
98 | 1530 | 7316 | Bingo | 0.167 | no |
99 | 1963 | 789 | Focus | 0.165 | no |
100 | 2011 | 108784 | Ascension: Storm of Souls | 0.163 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.86 |
Decision Tree | roc_auc | binary | 0.68 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think fionajackilarious is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
400 | 2136 | Pachisi | 0.757 | no |
1893 | 2386 | Chinese Checkers | 0.756 | no |
1959 | 7688 | Memory | 0.689 | no |
1861 | 11670 | The Game of Authors | 0.683 | no |
2016 | 205398 | Citadels | 0.666 | no |
What games does the model think fionajackilarious is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2000 | 5750 | Harry Potter and the Sorcerer's Stone Trivia Game | 0.000 | yes |
1937 | 2680 | Stock Ticker | 0.001 | yes |
2019 | 264055 | Draftosaurus | 0.001 | yes |
1960 | 148203 | Dutch Blitz | 0.001 | yes |
2015 | 187801 | Star Wars Trivia Game | 0.001 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Okiya | Cappuccino | Splendor | 504 | Citadels | Codenames: Disney – Family Edition | Everdell | Era: Medieval Age |
2 | Divinare | The Little Prince: Make Me a Planet | Five Tribes | Codenames | Scythe | Charterstone | Coimbra | Tapestry |
3 | Terra Mystica | Room 25 | Patchwork | Raptor | Smash Up: Cease and Desist | Breaking Bad: The Board Game | Coin & Crown | Last Bastion |
4 | Robinson Crusoe: Adventures on the Cursed Island | Legacy: The Testament of Duke de Crecy | Roll for the Galaxy | Star Realms: Colony Wars | Mr. Cabbagehead's Garden | Spirit Island | Micropolis | The Magnificent |
5 | Qin | Tash-Kalar: Arena of Legends | Onitama | The King Is Dead | When I Dream | Smash Up: What Were We Thinking? | Underwater Cities | Paris: La Cité de la Lumière |
6 | Targi | Empire Engine | Ultimate Werewolf | Exploding Kittens: NSFW Deck | Kingdomino | Gaia Project | Prowler's Passage | Queenz: To Bee or Not to Bee |
7 | Love Letter | The Builders: Middle Ages | Abyss | 7 Wonders Duel | Kanagawa | Twilight Imperium: Fourth Edition | New Frontiers | Ticket to Ride: London |
8 | Tokaido | Hanamikoji | AquaSphere | Barony | Ticket to Ride: Rails & Sails | Jump Drive | Kero | Chai |
9 | Game of Thrones: The Card Game | City of Remnants | Port Royal | Salem 1692 | Codenames: Deep Undercover | Century: Spice Road | Cosmic Run: Regeneration | Amul |
10 | Smash Up | They Who Were 8 | Smash Up: Monster Smash | Lumis: Der Pfad des Feuers | Dream Home | Best of Werewolves of Miller's Hollow | Rising Sun | Carnival of Monsters |
11 | Noblemen | La Boca | Roll Through the Ages: The Iron Age | Stac | Bloodborne: The Card Game | Richard the Lionheart | Between Two Castles of Mad King Ludwig | Watergate |
12 | Il Vecchio | UGO! | Imperial Settlers | Mafia de Cuba | Conan | Codenames: Duet | Les Aventuriers du Rail Express | Herbaceous Sprouts |
13 | The Manhattan Project | Munchkin Legends | Istanbul | Smash Up: Munchkin | Exceed Fighting System | Exploding Kittens: Newbie Edition | Codenames: Harry Potter | Robin of Locksley |
14 | SłowoStwory | Two Rooms and a Boom | King of New York | Lanterns: The Harvest Festival | 13 Days: The Cuban Missile Crisis | Gloomhaven | Lords of Hellas | Hey Robot |
15 | Yedo | Ici Londres | Fields of Arle | Mombasa | Agricola (Revised Edition) | Santa Maria | Azul: Stained Glass of Sintra | Azul: Summer Pavilion |
16 | The Resistance: Avalon | Glass Road | Dragon's Hoard | Love Letter: The Hobbit – The Battle of the Five Armies | Dale of Merchants 2 | LYNGK | Decrypto | Aftermath |
17 | Pixel Tactics | Lewis & Clark: The Expedition | Sheriff of Nottingham | Dale of Merchants | Aeon's End | Tesla vs. Edison: Duel | Papering Duel | Western Empires |
18 | [_BLÄNK] | Smash Up: The Obligatory Cthulhu Set | Jäger und Späher | ...and then, we held hands. | Arkham Horror: The Card Game | Circle the Wagons | Hokkaido | Combo Color |
19 | Android: Netrunner | Anomia: Party Edition | Continental Express | Circle of Life | Slush Fund 2 | Majesty: For the Realm | Duelosaur Island | Villagers |
20 | Shadows over Camelot: The Card Game | Smash Up: Awesome Level 9000 | Haru Ichiban | Between Two Cities | Bücherwurm | Herbaceous | Ticket to Ride: New York | Century: A New World |
21 | Morels | Veletas | Onirim (Second Edition) | Roots: A Game of Inventing Words | Hit Z Road | Ticket to Ride: Germany | Seasons of Rice | Tiny Towns |
22 | Noah | Capo Dei Capi | Paperback | Holmes: Sherlock & Mycroft | Dead of Winter: The Long Night | Werewords | Renegade | Penny Rails |
23 | Mage Wars Arena | Longhorn | Smash Up: Science Fiction Double Feature | Epic Card Game | Honshū | Wordstacker | Hardback | Wingspan |
24 | Agricola: All Creatures Big and Small | Bomb Squad | Emperor's New Clothes | Plums | Tides of Madness | Sagrada | Leviathan | Slyville |
25 | Cockroach Poker Royal | Guildhall: Job Faire | Three Kingdoms Redux | Risk: Europe | Eschaton | Photosynthesis | Blackout: Hong Kong | A Rusty Throne |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
fionajackilarious | owned | validation | GLM | roc_auc | 0.840 |
fionajackilarious | owned | validation | Decision Tree | roc_auc | 0.598 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 320505 | Mattock | 0.310 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.132 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.127 | no |
2020 | 319966 | The King Is Dead: Second Edition | 0.126 | yes |
2020 | 312804 | Pendulum | 0.124 | no |
2020 | 256317 | Guild Master | 0.104 | no |
2020 | 299571 | Bandida | 0.102 | no |
2020 | 300010 | Dragomino | 0.093 | no |
2020 | 300877 | New York Zoo | 0.090 | yes |
2020 | 309630 | Small World of Warcraft | 0.088 | no |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.083 | no |
2020 | 318983 | Faiyum | 0.076 | no |
2020 | 299592 | Beez | 0.068 | no |
2020 | 311927 | Long Live the King: A Game of Secrecy and Subterfuge | 0.067 | no |
2020 | 317847 | The Lost Words | 0.067 | no |
2020 | 316554 | Dune: Imperium | 0.059 | no |
2020 | 295486 | My City | 0.057 | yes |
2020 | 295687 | Trust Me, I'm a Doctor | 0.056 | no |
2020 | 293014 | Nidavellir | 0.052 | no |
2020 | 293678 | Stellar | 0.052 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.051 | no |
2020 | 299179 | Chancellorsville 1863 | 0.051 | no |
2020 | 298638 | Sheriff of Nottingham: 2nd Edition | 0.048 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.046 | no |
2020 | 300322 | Hallertau | 0.046 | no |
2020 | 250725 | Thrive | 0.044 | no |
2020 | 294294 | Letterpress | 0.040 | yes |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.037 | no |
2020 | 308416 | Tapeworm | 0.036 | no |
2020 | 270109 | Iwari | 0.035 | yes |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.034 | no |
2020 | 293296 | Splendor: Marvel | 0.033 | no |
2020 | 295948 | Aqualin | 0.033 | no |
2020 | 284217 | Rush M.D. | 0.033 | no |
2020 | 296626 | Sonora | 0.032 | yes |
2020 | 283155 | Calico | 0.032 | yes |
2020 | 303553 | Skulls of Sedlec | 0.032 | no |
2020 | 309110 | Food Chain Island | 0.031 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.031 | no |
2020 | 303552 | Magic: The Gathering – Unsanctioned | 0.029 | no |
2020 | 299252 | Here to Slay | 0.028 | no |
2020 | 284742 | Honey Buzz | 0.028 | yes |
2020 | 229782 | Roland Wright: The Dice Game | 0.028 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.028 | no |
2020 | 298017 | Treelings | 0.027 | no |
2020 | 278042 | Crusader Kingdoms: The War for the Holy Land | 0.026 | no |
2020 | 286021 | Free Market: NYC | 0.024 | no |
2020 | 320819 | Dinner in Paris | 0.024 | no |
2020 | 299452 | Dale of Merchants 3 | 0.024 | no |
2020 | 311193 | Anno 1800 | 0.024 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2022 | 310873 | Carnegie | 0.370 | no |
2021 | 332944 | Sobek: 2 Players | 0.335 | no |
2021 | 344258 | That Time You Killed Me | 0.292 | no |
2021 | 291859 | Riftforce | 0.257 | no |
2021 | 326804 | Rorschach | 0.252 | no |
2021 | 314088 | Agropolis | 0.224 | no |
2021 | 331635 | Kameloot | 0.221 | no |
2021 | 300148 | Spy Connection | 0.176 | no |
2022 | 331106 | The Witcher: Old World | 0.171 | no |
2021 | 343905 | Boonlake | 0.165 | no |
2021 | 295947 | Cascadia | 0.160 | no |
2021 | 339906 | The Hunger | 0.158 | no |
2021 | 330036 | Great Plains | 0.131 | yes |
2021 | 328479 | Living Forest | 0.130 | no |
2021 | 285967 | Ankh: Gods of Egypt | 0.124 | no |
2021 | 329465 | Red Rising | 0.123 | no |
2022 | 304051 | Creature Comforts | 0.118 | no |
2021 | 348461 | Castle Break | 0.115 | no |
2021 | 290236 | Canvas | 0.114 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.113 | no |
2021 | 339789 | Welcome to the Moon | 0.111 | no |
2021 | 308989 | Bristol 1350 | 0.106 | no |
2021 | 316287 | Quest | 0.100 | no |
2023 | 352574 | Fit to Print | 0.098 | no |
2021 | 304333 | Zoollywood | 0.095 | no |
2022 | 352454 | Trailblazers | 0.087 | no |
2021 | 287608 | Epic Card Game: Duels | 0.084 | no |
2021 | 338980 | Eastern Empires | 0.083 | no |
2021 | 324242 | Sheepy Time | 0.083 | no |
2021 | 286632 | Blood of the Northmen | 0.081 | no |
2021 | 333553 | For the King (and Me) | 0.072 | no |
2021 | 340041 | Kingdomino Origins | 0.072 | no |
2021 | 302911 | Mein Königreich für ein Pferd | 0.068 | no |
2021 | 325698 | Juicy Fruits | 0.065 | no |
2021 | 307862 | Dollars to Donuts | 0.064 | no |
2021 | 342942 | Ark Nova | 0.063 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.060 | no |
2021 | 339484 | Savannah Park | 0.060 | no |
2021 | 334644 | Nicodemus | 0.059 | no |
2021 | 329873 | GROVE: A 9 card solitaire game | 0.059 | no |
2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.059 | no |
2021 | 318560 | Witchstone | 0.058 | no |
2021 | 344277 | Corrosion | 0.058 | no |
2021 | 331685 | Hit the Silk! | 0.057 | no |
2021 | 342246 | Feuding Foodies | 0.056 | no |
2022 | 351605 | Bohnanza: 25th Anniversary Edition | 0.056 | no |
2021 | 221298 | NewSpeak | 0.056 | no |
2021 | 295607 | Canopy | 0.053 | no |
2022 | 284118 | Mechanical Beast | 0.053 | no |
2021 | 338834 | MicroMacro: Crime City – Full House | 0.051 | no |
2021 | 340909 | Gloomholdin' | 0.051 | no |
2021 | 341358 | INSERT | 0.051 | no |
2022 | 316090 | Vivid Memories | 0.050 | no |
2021 | 305682 | Rolling Realms | 0.050 | no |
2021 | 286667 | Tutankhamun | 0.049 | no |
2021 | 314491 | Meadow | 0.049 | no |
2022 | 334065 | Verdant | 0.049 | no |
2021 | 319792 | Fly-A-Way | 0.048 | no |
2021 | 343696 | Dune: Betrayal | 0.048 | no |
2022 | 330786 | Blokk! | 0.046 | no |
2021 | 315767 | Cartographers Heroes | 0.045 | no |
2021 | 306169 | MATCH 5 | 0.044 | no |
2021 | 337787 | Summer Camp | 0.044 | no |
2021 | 336929 | Land vs Sea | 0.043 | no |
2021 | 283242 | The Whatnot Cabinet | 0.043 | no |
2021 | 313841 | Lunar Base | 0.042 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.042 | no |
2022 | 251661 | Oathsworn: Into the Deepwood | 0.041 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.041 | no |
2021 | 337389 | Snakesss | 0.041 | no |
2021 | 304324 | Dive | 0.040 | no |
2021 | 257706 | Zoo-ography | 0.040 | no |
2021 | 309319 | Apogee | 0.039 | no |
2021 | 304985 | Dark Ages: Holy Roman Empire | 0.039 | no |
2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.039 | no |
2021 | 313730 | Harsh Shadows | 0.039 | no |
2021 | 322014 | All-Star Draft | 0.038 | no |
2021 | 321596 | P'achakuna | 0.038 | no |
2021 | 326727 | Card Rails | 0.038 | no |
2021 | 300523 | Biblios: Quill and Parchment | 0.038 | no |
2021 | 339790 | Cocktail | 0.037 | no |
2022 | 258779 | Planet Unknown | 0.036 | no |
2022 | 349793 | Age of Rome | 0.034 | no |
2021 | 300305 | Nanga Parbat | 0.034 | no |
2022 | 332393 | Bridge City Poker | 0.034 | no |
2021 | 327711 | It's a Wonderful Kingdom | 0.034 | no |
2021 | 322560 | Maeshowe: an Orkney Saga | 0.033 | no |
2021 | 309250 | Empyrean Hero: The Card Game | 0.033 | no |
2022 | 283137 | Human Punishment: The Beginning | 0.031 | no |
2021 | 283387 | Rocketmen | 0.030 | no |
2021 | 303954 | Pax Viking | 0.030 | no |
2022 | 317511 | Tindaya | 0.030 | no |
2022 | 338460 | The Isle of Cats: Explore & Draw | 0.030 | no |
2021 | 334782 | Bayou Bash | 0.029 | no |
2021 | 337195 | Growing Season | 0.029 | no |
2021 | 334307 | Clash of Decks: Starter Kit | 0.029 | no |
2021 | 333144 | Stronghold: Undead (Second Edition) | 0.029 | no |
2021 | 249277 | Brazil: Imperial | 0.029 | no |
2022 | 320718 | Hidden Leaders | 0.029 | no |
2021 | 340455 | King of the Valley | 0.029 | no |